Ehecatl Antonio del Rio-Chanona

According to our database, Ehecatl Antonio del Rio-Chanona authored at least 38 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Machine learning in process systems engineering: Challenges and opportunities.

Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, R. Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangöz, Ali Mesbah, Fani Boukouvala, Fernando V. Lima, Ehecatl Antonio del Rio-Chanona, Christos Georgakis

Comput. Chem. Eng., February, 2024

2023

Multi-fidelity data-driven design and analysis of reactor and tube simulations.

Tom Savage, Nausheen Basha, Jonathan McDonough, Omar K. Matar, Ehecatl Antonio del Rio-Chanona

Comput. Chem. Eng., November, 2023

Tube-based distributionally robust model predictive control for nonlinear process systems via linearization.

Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

Comput. Chem. Eng., February, 2023

Linearizing nonlinear dynamics using deep learning.

Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

Comput. Chem. Eng., February, 2023

Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems.

Tom Savage, Ehecatl Antonio del Rio-Chanona

CoRR, 2023

Hierarchical planning-scheduling-control - Optimality surrogates and derivative-free optimization.

Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona

CoRR, 2023

ARRTOC: Adversarially Robust Real-Time Optimization and Control.

Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

CoRR, 2023

Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation.

Tom Savage, Nausheen Basha, Jonathan McDonough, Omar K. Matar, Ehecatl Antonio del Rio-Chanona

CoRR, 2023

An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems.

Marwan Mousa, Damien van de Berg, Niki Kotecha, Ehecatl Antonio del Rio-Chanona, Max Mowbray

CoRR, 2023

Distributional constrained reinforcement learning for supply chain optimization.

Jaime Sabal Bermúdez, Antonio del Rio-Chanona, Calvin Tsay

CoRR, 2023

The Automated Discovery of Kinetic Rate Models - Methodological Frameworks.

Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, Klaus Hellgardt, King Kuok Hii, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

CoRR, 2023

An efficient data-driven distributionally robust MPC leveraging linear programming.

Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

Proceedings of the American Control Conference, 2023

2022

Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning.

Cesare Caputo, Michel-Alexandre Cardin, Pudong Ge, Fei Teng, Anna Korre, Ehecatl Antonio del Rio-Chanona

CoRR, 2022

Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression.

Marta A. Zagorowska, M. Degner, Lukas Ortmann, Akhil Ahmed, Saverio Bolognani, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

CoRR, 2022

Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors.

Tom Savage, Nausheen Basha, Omar K. Matar, Ehecatl Antonio del Rio-Chanona

CoRR, 2022

Neural ODEs as Feedback Policies for Nonlinear Optimal Control.

Ilya Orson Sandoval, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona

CoRR, 2022

Learning Linear Representations of Nonlinear Dynamics Using Deep Learning.

Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

CoRR, 2022

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes.

Max Mowbray, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

CoRR, 2022

A hybrid data-driven and mechanistic model soft sensor for estimating CO₂ concentrations for a carbon capture pilot plant.

Yilin Zhuang, Yixuan Liu, Akhil Ahmed, Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Colin P. Hale, Mehmet Mercangöz

Comput. Ind., 2022

Safe chance constrained reinforcement learning for batch process control.

Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona, Dongda Zhang

Comput. Chem. Eng., 2022

Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station.

Akhil Ahmed, Marta A. Zagorowska, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

Proceedings of the European Control Conference, 2022

2021

A geometric deep learning approach to predict binding conformations of bioactive molecules.

Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona, Jörg Kurt Wegner

Nat. Mach. Intell., 2021

Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes.

Eric Bradford, Lars Imsland, Marcus Reble, Ehecatl Antonio del Rio-Chanona

CoRR, 2021

Integrating process design and control using reinforcement learning.

Steven Sachio, Max Mowbray, Maria M. Papathanasiou, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

CoRR, 2021

Data-driven distributionally robust MPC using the Wasserstein metric.

Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

CoRR, 2021

Safe Chance Constrained Reinforcement Learning for Batch Process Control.

Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio-Chanona, Robin Smith, Dongda Zhang

CoRR, 2021

Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation.

Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat

Comput. Chem. Eng., 2021

Constrained model-free reinforcement learning for process optimization.

Elton Pan, Panagiotis Petsagkourakis, Max Mowbray, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

Comput. Chem. Eng., 2021

Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes.

Panagiotis Petsagkourakis, Benoît Chachuat, Ehecatl Antonio del Rio-Chanona

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization.

Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford, Jose Eduardo Alves Graciano, Benoît Chachuat

CoRR, 2020

Chance Constrained Policy Optimization for Process Control and Optimization.

Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Federico Galvanin, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

CoRR, 2020

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty.

Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

CoRR, 2020

Reinforcement learning for batch bioprocess optimization.

Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

Comput. Chem. Eng., 2020

Stochastic data-driven model predictive control using gaussian processes.

Eric Bradford, Lars Imsland, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

Comput. Chem. Eng., 2020

2019

Nonlinear model predictive control with explicit back-offs for Gaussian process state space models.

Eric Bradford, Lars Imsland, Ehecatl Antonio del Rio-Chanona

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes.

Eric Bradford, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, Ehecatl Antonio del Rio-Chanona

Comput. Chem. Eng., 2018

2017

On the solution of differential-algebraic equations through gradient flow embedding.

Ehecatl Antonio del Rio-Chanona, Craig Bakker, Fabio Fiorelli, Michail Paraskevopoulos, Felipe Scott, Raúl Conejeros, Vassilios S. Vassiliadis

Comput. Chem. Eng., 2017

2016

Automated structure detection for distributed process optimization.

Ehecatl Antonio del Rio-Chanona, Fabio Fiorelli, Vassilios S. Vassiliadis

Comput. Chem. Eng., 2016